What is the difference between regularization and overfitting in Python?

What is the difference between regularization and overfitting in Python?

Regularization is a technique used to prevent overfitting by adding a penalty to the loss function. Overfitting occurs when a model learns the noise in the training data instead of the actual pattern.

Can you explain how regularization techniques like L1 and L2 work?

Sure! L1 regularization adds the absolute value of the coefficients as a penalty to the loss function, promoting sparsity. L2 regularization adds the squared value of the coefficients, which helps to reduce the magnitude of the coefficients.

How can I implement regularization in Python using scikit-learn?

You can implement regularization in scikit-learn using models like Ridge for L2 regularization and Lasso for L1 regularization. Just import the model and set the alpha parameter to control the strength of the regularization.

What are some signs that my model is overfitting?

Signs of overfitting include a high accuracy on the training set but significantly lower accuracy on the validation set, as well as a model that performs poorly on unseen data.